Suma Mohamadpur; Hamed Rouhani; Hojat Ghorbani Vaghei; Seyed Morteza Seyedian; Abulhasan Fath Abadi
Abstract
In many semi-arid regions of Iran, soil erosion has turned into a serious environmental problem affecting land productivity, nutrient loss, water quality, and fresh water ecosystems. Rates of soil loss differ according to erosion type and land degradation processes. Rill erosion is commonly observed ...
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In many semi-arid regions of Iran, soil erosion has turned into a serious environmental problem affecting land productivity, nutrient loss, water quality, and fresh water ecosystems. Rates of soil loss differ according to erosion type and land degradation processes. Rill erosion is commonly observed when rainstorms occur on steep slopes and sediment transport in rill flows exhibits the characteristics of non-equilibrium transport. In this paper, sediment concentration of rill flow is estimated by adaptive neuro-fuzzy inference system (ANFIS). A series of mathematical equations and parameters affecting rill hydrodynamics and soil detachment were used for well-defined rill sediment concentration. A series of filed experiments were performed to evaluate the model. The stepwise method was used to select the most important and effective input variables from measured input parameters of soil properties, topographic and vegetation attributes affecting sediment concentration of rill flow. Based on the stepwise procedure, the most significant parameters in the model predications were steep slope, vegetation percentage, clay percentage, and shear stress parameters. The values of sediment concentration simulated by the model were in agreement with observed values with Coefficient of Correlation (R2), Root Mean Square Error (RMSE) and Mean Bias Error (MBE) of 0.697, 30.5 and 1.0, respectively. The results of the investigation shows that the data-driven ANFIS modeling approach can be a powerful alternative technique for correctly estimating rill sediment concentration.
Sadegh Tali-Khoshk; Mohsen Mohseni Saravi; Mahadi Vatakhah; Shahram Khalighi-Sigarodi
Abstract
Because of insufficient factors including facilities, budget, human resources as well as time watershed operation is not applicable throughout the basin. As a result, watershed operation should be performed in the sub-basins in which is more affectionate and the risk frequency of some events such as ...
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Because of insufficient factors including facilities, budget, human resources as well as time watershed operation is not applicable throughout the basin. As a result, watershed operation should be performed in the sub-basins in which is more affectionate and the risk frequency of some events such as destruction, degradation; physical and financial damage and also flooding are considerably high. In addition, due to hydrometric stations, defects or the lack of stations in some areas, some efforts have been made experts recently to assess and consequently introduce some novel and reliable methods for prioritizing on the basis of current data obtained from sub-basins features of different geographical regions. In current study, the utilization possibilities of neuro-fuzzy technique and SCS in HEC-HMS model that have different potential to examine a wide range of advantageous and disadvantageous in making various decisions were studied. To determine the prediction accuracy of these methods, the rate of flow and sediment output of Taleghan sub-basins were taken over one year. The results of these methods were then compared with the maximum two-year return period flow observations. Our results revealed that in making prioritization, neuro-fuzzy as compared with the SCS method can produce the best prediction as long as the coefficients of errors, efficiency compared to the observational data and predictions are taken into account.
Ruollah Taghizadeh Mehrjardi; Fereydoon Sarmadian; Gholem Reza Savaghebi; Mahmoud Omid; Nourayer Toomanian; Mohammad Javad Rousta; Mohammad Hasan Rahimiyan
Abstract
In recent years, alternative methods have been used for estimation of soil salinity. Therefore, at present research, 600 soil samples collected from Ardakan in central Iran. Then EM38 and terrain parameters such as wetness index, land index and curvature as readily measured properties and soil salinity ...
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In recent years, alternative methods have been used for estimation of soil salinity. Therefore, at present research, 600 soil samples collected from Ardakan in central Iran. Then EM38 and terrain parameters such as wetness index, land index and curvature as readily measured properties and soil salinity (0-30 and 0-100) as predicted variables were measured. After that, the data set was divided into two subsets for calibration (80%) and testing (20%) of the models. For predicting of mentioned parameters, ANFIS, GA, ANNs and MLR were applied. In order to evaluate models, some evaluation parameters such as root mean square, average error, average standard error and coefficient of determination were used. Results showed that the ANFIS model gives better estimation than the other techniques for all characteristics whereas this model increased accuracy of predictions about 17 and 11% for EC30 and EC100 respectability. After ANFIS model, GA and ANN had better accuracy than multivariate regression.
